Method for cross-diagnostic identification and treatment of neurologic features underpinning mental and emotional disorders

ABSTRACT

A system and method for diagnosing mental or emotional disorders is disclosed. An affective BCI component is incorporated into a closed loop, symptom—responsive psychiatric DBS system. A series of input data related to a brain of the patient is acquired while the patient performs a battery of behavioral tasks. From the patient&#39;s performance on the task battery, the system identifies what is abnormal for that individual patient in terms of functional domains. Patient-specific behavioral measurements are then linked to patterns of activation and de-activation across different brain regions, identifying specific structures that are the source of the patient&#39;s individual impairment.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority from U.S. Patent Application No.61/984,416 filed Apr. 25, 2014 and U.S. Patent Application No.61/984,466 filed Apr. 25, 2014.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under cooperativeagreement W911NF-14-2-0045 awarded by the Defense Advanced ResearchProjects Agency (DARPA). The government has certain rights to theinvention.

BACKGROUND OF THE INVENTION

This invention relates to systems and methods for cross-diagnosticidentification and treatment of neurological features underpinningmental and emotional disorders. More particularly, this inventionrelates to a multi-modal imaging technique utilized while administeringa transdiagnostic assessment battery to a patient to identify one ormore brain regions associated with an emotional or mental disorder. Thespecific brain region may then be targeted for treatment with brainstimulation techniques.

Mental illnesses, for example, post traumatic stress disorder (PTSD),depression, and addiction, impair war fighters and civilians, and are aleading cause of disability and lost productivity. These illnesses canbe conceptualized as brain disorders of malfunctioning neural circuits.Often, psychiatric treatments fail to cure a substantial fraction ofpatients, who are then declared resistant to approved therapeuticinterventions. At the core of the problem is the focus on historicaldiagnostic categories. The National Institute of Mental Health's (NIMH)Research Domain Criteria (RDoC) project aims to developneuroscience-based classification schemes for diagnosis and treatment ofneural circuitry dysfunction. Diagnostic and Statistical Manual (DSM)diagnoses are not neurobiologic entities, but are a historicalchecklist-based approach of clustering symptoms used to definehypothetical constructs or syndromes. Those syndromes may not align withunderlying neurobiological dysfunction in neural circuitry andcorresponding behavioral (functional) domains.

Thus, attempts have been made using responsive brain stimulation systemsto treat mental and emotional disorders previously treated bypsychiatrists. Responsive brain stimulation is stimulation applied tothe brain that responds directly to a patient's electrical brainactivity or clinical features. One realization of a responsive brainstimulation system is implantable, with electrodes placed inside apatient's brain. There are a number of sites in the brain wherestimulation may be applied in attempts to change a patient's emotionalexperiences. However, these responsive brain stimulation systems oftenhave no proven biomarker. A biomarker may be a measurable indicator orsignal from the brain or body representative of the symptoms of theillness being treated that indicates whether the symptoms have gottenbetter or worse. Without something reliable to sense, it is difficultfor the responsive stimulator to respond accurately.

Other attempts to treat mental and emotional disorders have moved awayfrom trying to find biomarkers for specific mental disorders, andinstead have tried to find biomarkers for emotions utilizing anaffective brain-computer interface (aBCI). An aBCI in combination with abrain scanner or electroencephalography (EEG) system, for example, canlook at signals in real-time and determine whether the subject is havinga positive-valence (e.g., happy, pleasant, etc.) or a negative-valence(e.g., angry, afraid, unpleasant, etc.) emotion. In more advancedsystems, the specific emotion (e.g., anger, fear, disgust, pleasure,etc.) can be classified.

However, an aBCI alone may not be a useful clinical tool, as it cannotdetermine whether the emotion is a healthy emotion (e.g., anger that wasjustifiably provoked, fear because the patient is in a dangeroussituation, etc.) from an unhealthy emotion (e.g., violent anger inresponse to a mild insult, fear of an ordinarily safe situation such asdriving on a freeway, etc.). Therefore, there it is difficult for acontroller to decide whether the emotion should be corrected or alteredby stimulating the brain.

Thus, there is a clinical need for responsive neurostimulators, whichsense a patient's brain activity and deliver targeted electricalstimulation to suppress unwanted symptoms. This is particularly true inpsychiatric illness, where symptoms can fluctuate throughout the day.Affective BCIs, which decode emotional experience from neural activity,are a candidate control signal for responsive stimulators targeting thelimbic circuit. Present affective decoders, however, cannot yetdistinguish pathologic from healthy emotional extremes. Indiscriminatestimulus delivery would reduce quality of life and may be activelyharmful.

The need for affective BCI monitoring and decoding is clearest in deepbrain stimulation (DBS). Psychiatric DBS has been used at multipletargets, with preliminary success in treating depression andobsessive-compulsive disorder (OCD), for example. Progress inpsychiatric DBS, however, has been limited by its inherent open-loopnature. Present open-loop DBS systems deliver energy continuously at apre-programmed frequency and amplitude, with parameter adjustments onlyduring infrequent clinician visits. This has led to more rapid depletionof device batteries which requires battery replacement surgeries andintroduces the patient to associated pain and/or infection. Thecontinuous delivery of energy also leads to an increased side-effectburden. Side effects in particular derive from present devices'inability to match stimulation to a patient's current affective state,brain activity, and therapeutic need. Atop this, many disorders havesymptoms that rapidly flare and remit, on a timescale of minutes tohours. This is particularly common in the anxiety and trauma relatedclusters. Existing open-loop DBS strategies have been unable toeffectively treat such fluctuations, because the fluctuations occur onshorter timescales than the infrequent clinical visits.

However, development of closed-loop emotional DBS systems has beenblocked by a lack of accurate or feasible biomarkers. Three majorchallenges arise when considering existing affective BCIs as the sensingcomponent of closed-loop DBS control. First, many identified neuralcorrelates of affective disorders cannot be continuously monitored inthe community. Functional magnetic resonance imaging (fMRI) can providedeep insights into activity across the whole brain, and has beendemonstrated for partial affective classification in real time. Similarresults have been seen with near-infrared spectroscopy (NIRS), whichalso measures blood-oxygenation signals. The former, however, requiresbulky machines and is not compatible with implanted devices, and thelatter has not yet been demonstrated in an online-decoding paradigm.Moreover, although NIRS can be reduced to a wearable/portable device, itrequires an externally worn headset. Given the unfortunate persistenceof stigma attached to patients with mental disorders, few would wear avisible display of their illness, even if it did control symptoms.

Another challenge with existing affective BCIs is that affectivedecoding modalities that support continuous recording may not functionproperly in the presence of psychiatric illness. Electrocorticography(ECOG) is a promising approach, as it can be implanted, and thus hidden,with relatively minimally invasive surgery. ECOG signals offer temporalresolution and may be able to use decoders originally developed forelectroencephalography (EEG). Non-invasive EEG has been a successfulapproach in affective BCI, with some real-time decoding of emotionalinformation. Uncertainty arises because all successful EEG affectivedecoding has been demonstrated in healthy patients. Patients with mentalillness, particularly those with treatment-resistant disorders, bydefinition do not have normal or healthy neurologic function.Furthermore, recent experiences with EEG in psychiatry suggest thatmeasures that accurately decode healthy controls may not transfer topatients. EEG biomarkers that initially appeared to correlate withpsychiatric symptoms and treatment response have often not held up underreplication studies. This is at least in part because psychiatricdiagnosis focuses on syndromes and symptom clusters, not etiologies.There is a wide consensus that clinical diagnoses generally containmultiple neurologic entities, and that the same clinical phenotype mightarise from diametrically opposite changes in the brain. This may presenta challenge for clinical translation of existing affective decoders.

Yet another challenge with existing affective BCIs is that even ifaffective BCIs can function in the presence of clinical symptoms, theymay not be able to adequately distinguish pathologic states. Neweraffective BCI algorithms may yet be shown to accurately classify emotioneven in the presence of abnormal neural circuit activity, but this isonly part of the need. Psychiatric disorders are marked by extremes ofthe same emotions that occur in everyday normal life. The difference isnot the degree or type of affect, but its appropriateness to thecontext. PTSD is one clear example where patients with this disorderover generalize from a fearful event and experience high arousal andvigilance in contexts that are objectively safe. It is likely possiblefor an affective BCI to detect high arousal in a patient with PTSD inuncontrolled real-world environments. It is less clear whether anyalgorithm could distinguish pathologic arousal (e.g., a ‘flashback’ in agrocery store, confrontation with trauma cues, etc.) from healthyvariance (e.g., riding a roller coaster, watching an exciting movie,etc.). These emotions would be very difficult to differentiate solely onthe basis of experienced affect, and yet the use of brain stimulation toneutralize the latter set of experiences would negatively impact thepatient's quality of life.

The above described challenges combine to reveal a final complication.In a fully implanted system, onboard storage and computational resourcesare limited, and therefore it may not be possible to perform decodingand tracking over long periods of time. Thus, affective decoders arecaught in a dilemma of temporal resolution. If the affective decodersare tuned to respond to brief but intense events, the decoders mayover-react to natural and healthy emotional variation. If the decodersinstead focus only on detecting and compensating for long-term trends,sharp but short exacerbations will go uncorrected, decreasing patients'quality of life and continuing the problems of existing open-loop DBS.In the very long run, these problems may be ameliorated by improvementsin battery and processing technology. However, regulatory agenciesrequire extensive review of all new technology components, meaning thata new battery could take a decade to reach clinical use even after beingsuccessfully demonstrated for non-implantable applications. Processorsmight be more easily upgraded, but increased processing power meansincreased heat, which cannot be readily dissipated inside the body.

Thus, there is a need for systems and methods for responsive decodingand stimulation capable of operating within the limits of currentclinical technology. An affective BCI usable as the sensing component ofa responsive brain stimulator and capable of inferring emotional statefrom neural signals to enable a responsive, closed-loop stimulator isdesirable. It is also desirable for continuous monitoring capable ofindicating that the system is moving into a pathological state so thatthe controller can adjust parameters of an implanted DBS to counteractthat trajectory, as well as reduce the side effects of over-stimulation,alleviate residual symptoms that may relate to under-stimulation, andimprove power consumption for a longer battery life.

SUMMARY OF THE INVENTION

The invention overcomes the aforementioned drawback by providing asystem and method for treating patients with mental or emotionaldisorders utilizing an affective BCI component in a closed-loop,symptom-responsive psychiatric DBS system. Plasticity and volitioncomponents are incorporated into the affective BCI for control ofneurostimulation. The systems and methods may be useful for patientswith symptoms in the mood (e.g., depression, bipolar disorder, etc.),anxiety (e.g., generalized anxiety, panic disorder, etc.),obsessive-compulsive (e.g., obsessive-compulsive disorder, Tourettesyndrome, etc.), and trauma/stress-related (e.g., post-traumatic stressdisorder, etc.) clusters, as well as patients with certain types ofbrain damage, such as white matter injury.

Regarding plasticity, in some embodiments, a BCI may be built by havingparticipants perform a ‘predicate task’, such as hand movement, motorimagery, or emotional imagery in the case of affective BCI. Thetransdiagnostic tasks described below may also be considered aspredicate tasks. From neural activity during this training period, adecoder is built, then deployed to classify new brain activity as itarises. As the BCI learns the mapping between brain signals and taskvariables, the brain changes to better match the decoder. The brain'sinherent capacity for plasticity will remap cortical signals to produceimproved information for the BCI. It is also possible to create a BCIwhere there is no predicate task, and where the decoder is simplyinitialized with arbitrary parameters. This is sometimes referred to asa ‘direct control’ system, and has been shown to be an effective way ofbuilding a BCI that is computationally efficient. The brain stillchanges and learns to use this type of decoder effectively.

The volition component of affective BCI in closed-loop psychiatric DBSincludes a patient sensing that present stimulation parameters are notwell matched to his/her clinical needs, then choosing to alter thestimulation parameters by deliberately modulating specific aspects ofbrain activity (e.g., firing rates of specific neurons, power in certainbands of local field potential (LFP), EEG, ECOG, and the like). The listis not exhaustive; a wide range of standard signal transforms couldfeasibly and reasonably be applied to the signals and could be under apatient's volitional control. The volitionally controlled component mayresolve some of the limitations identified above, such that affectivedecoding would not need to directly classify an emotion as pathologicvs. healthy-extreme. Rather, the patient can express his/her desiredirectly and efficiently through a BCI, and the question of whether tooptimize response to fast or slow time-scales becomes moot. Thus,stimulation can be adjusted when the patient explicitly requests theaffective controller to do so. Heterogeneity of biomarkers withinclinical disorders may also be controlled for, because the primarydecoded variable is the patient's own intention to receive mood alteringneurostimulation. That said, with adequate application of thetransdiagnostic methods detailed below, it may be less necessary tocontrol for heterogeneity, because the clinical diagnosis will not bethe principal method for selecting an intervention.

In one aspect, the invention provides a method for diagnosing mental oremotional disorders. The method includes administering, using a computerinterface, a transdiagnostic assessment to a patient, thetransdiagnostic assessment including at least one psycho-physical task.A series of input data related to a brain of the patient is acquiredwhile the patient performs the at least one psycho-physical task. Animpairment corresponding to the patient along a set of functionaldomains is determined. A signal recorder is used to record at least oneof electrical, magnetic, optical, bio-acoustic, brain derived or bodyderived activity from the patient. The method further includesidentifying, from the at least one psycho-physical task, a deviation inthe set of functional domains as compared to a predetermined set ofcontrol subjects. Brain regions and signal characteristics areidentified from the input data corresponding to the identifieddeviation. One or more stimulation devices are used to apply stimulationto the identified brain regions to alter the at least one of electrical,magnetic, bio-acoustic, brain derived or body derived activity in thebrain regions and their connected regions.

In another aspect, the invention provides a system for diagnosing mentalor emotional disorders. The system includes a computer interface foradministering a transdiagnostic assessment to a patient, thetransdiagnostic assessment including at least one psycho-physical task.An acquisition device acquires a series of input data related to thebrain of the patient while the patient performs the at least onepsycho-physical task to determine an impairment corresponding to thepatient along a set of functional domains. A signal recorder records theat least one of electrical, magnetic, bio-acoustic, brain derived orbody derived activity from the patient. A processor is coupled to thecomputer interface and configured to identify, from the at least onepsycho-physical task, a deviation from the set of functional domains ascompared to a predetermined control, and identify brain regions andsignal characteristics from the input data corresponding to theidentified deviation. At least one stimulation device appliesstimulation to the identified brain regions to alter the at least one ofelectrical, optical, magnetic, bio-acoustic, brain derived or bodyderived activity in the brain regions. Stimulation may be applied fortherapeutic purposes, or to cause effects within the brain that probeits networks as they respond to the task.

These and other features, aspects, and advantages of the presentinvention will become better understood upon consideration of thefollowing detailed description, drawings and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system for diagnosing andtreating psychiatric patients using a transdiagnostic assessment andactivation of one or more brain regions in accordance with the presentinvention.

FIG. 2 is a block diagram of the system illustrated in FIG. 1.

FIG. 3 is an exploded view of a satellite module incorporated into thesystem of FIG. 2 for delivering stimulation to brain regions.

FIG. 4A is a schematic illustration of a controller hub incorporatedinto the system of FIG. 2 to be implanted under the scalp of a patientfor estimating the patient's psychiatric state and deliveringtherapeutic stimulation.

FIG. 4B is an exploded view of a housing for the controller hub of FIG.4A.

FIG. 4C is an exploded view of a battery and battery package forpowering the controller hub of FIG. 4A.

FIG. 5 is a schematic illustration of a head mounted interfaceincorporated into the system of FIG. 2.

FIG. 6 is a flow chart setting forth the steps of a method fordiagnosing and treating mental and emotional disorders by analyzing anindividuals brain with both imaging and behavioral testing in accordancewith the present invention.

FIG. 7 is a table of example functional domains for transdiagnosticassessment, corresponding functional tasks for probing each functionaldomain, and brain regions known to be involved in performance/impairmentfor each functional domain.

FIG. 8A is an image of an example transdiagnostic assessment task formeasuring the functional domain of fear extinction.

FIG. 8B is a graph illustrating a percentage of fear over various phasesof the transdiagnostic assessment task of FIG. 8A for a PTSD patient anda population average.

FIG. 9 is schematic illustration of an example transdiagnosticassessment task for measuring the functional domain of rewardmotivation.

FIG. 10 is schematic illustration of an example transdiagnosticassessment task for measuring the functional domain of emotionregulation.

FIG. 11A is a schematic illustration of an example transdiagnosticassessment task for measuring the functional domain of decision makingand impulsivity.

FIG. 11B is a graph illustrating a reaction time over various cardvalues assessed during the transdiagnostic assessment task of FIG. 11A.

FIG. 11C is a graph illustrating a percentage of high bets over variousstimulation categories during the transdiagnostic assessment task ofFIG. 11A.

FIG. 12A is schematic illustration of an example transdiagnosticassessment task for measuring the functional domain of attention andperseveration.

FIG. 12B is a graph illustrating a normalized reaction time over varioustrails including DBS stimulation during the transdiagnostic assessmenttask of FIG. 12A.

FIG. 13A is a schematic illustration of an example transdiagnosticassessment task for measuring the functional domain of cognitivecapacity.

FIG. 13B is a graph illustrating a percentage of correct trials forpatients receiving DBS stimulation in various brain regions over severaltrials while performing the transdiagnostic assessment task of FIG. 13A.

FIG. 14 is a schematic illustration of an analysis and interpretation ofa transdiagnostic assessment for two patients within a transdiagnosticdimensional space.

FIG. 15 is an image illustrating an example of patient-specific brainmapping relative to functional domains.

FIG. 16 is a schematic illustration of example implant sites for apatient with deficits in the functional domains of emotion regulationand fear extinction.

FIG. 17 is a schematic illustration of an example intention-decoding,BCI-based closed loop neurostimulator in accordance with the presentinvention.

FIG. 18A is a block diagram illustrating an example tethered recording,decoding, and stimulating system in accordance with another aspect ofthe present invention.

FIG. 18B is a schematic diagram illustrating a BCI algorithm to beincorporated into the system of FIG. 18A.

FIG. 18C is a graph illustrating a spike rate over time for successfuluse of the intentional control system compared to non-use of theintentional control system.

FIG. 19 is a graph illustrating success rates for BCI control over timefor an actual target acquisition rate, an on-line estimate ofchance-level performance, and an offline estimate of chance-levelperformance.

FIG. 20 is a schematic illustration of two examples of peri-stimulusdischarge rates on single channels involved in the prefrontal cortex(PFC) BCI.

Like reference numerals will be used to refer to like parts from Figureto Figure in the following description of the drawings.

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 1 and 2 show an affective BCI component in a closed-loop,symptom-responsive psychiatric DBS system 10. The system 10 may becapable of diagnosing patients not by clinical, symptom-focusedinterview, but by transdiagnostic assessment of objective measurement ofthe patient's performance on quantitative tests of psychologicalfunction. The system is configured to link the transdiagnostic symptomassessment to a treatment that specifically activates or de-activatesone or more brain areas via DBS. More specifically, the system 10 isdesigned to record and decode neural information from specificdysfunctional networks associated with specific symptoms, behaviors andthen deliver stimulation to these networks to afford symptom relief andmeasurable improvements in dysfunctional behaviors. The patient may begiven control over the stimulation system's actions through a hybrid BCIalgorithm that monitors the patient's intentions. Closed-loop algorithmsfurther focus the treatment not only in space (i.e., region of thebrain) but in time, so that stimulation occurs only when the patientneeds it.

The system 10 generally includes a central decoding and controlling hub12, connected satellite modules 14 that deliver stimulation andrecording through electrodes 16, for example, existing commercialelectrodes and innovative electrodes offering high channel counts withintegrated low-power signal conditioning. In some embodiments, the hub12 and satellite modules 14 may be a single implanted module. Eitherpart may also exist outside the body and communicate wirelessly to theelectrodes 16. The hub 12, or implanted module, may be implanted underthe scalp of a patient and can wirelessly communicate with an externalbase station 18 for data streaming, reprogramming, wireless recharge,and coordinating intervention across sites to enhance treatment ofneuropsychiatric dysfunction. Alternatively, the hub 12 may itself bewearable or otherwise non-implanted, or the hub 12 and base station 18may be merged as a single component. The base station 18 is inelectrical communication with the hub 12 via a head mounted interface 20and, in some embodiments, may interface with an offline processor 22having a user interface 24, such as a clinician interface. The headmounted interface 20 may be a wearable processing unit thatcommunicates, configures, and can control the implanted system 10. Thehead mounted interface 20 might also mount or implant to some other bodypart (e.g., chest) depending on the surgical clinician's preference. Insome embodiments, a hand held patient controller (e.g., a watch) may beprovided for self-reporting and triggering recordings, as well asmonitoring heart rate wirelessly, skin conductance, and the like.

The implantable system 10 may be designed to record and stimulate braincircuits. The configuration shown in FIGS. 1 and 2 is an example ofbilateral electrode satellite placement. In one non-limiting example,some of the electrodes 16 may be recording electrodes that consist ofcortical ECoG arrays recording from the dorsolateral prefrontal cortex(dIPFC) and subcortical DBS leads recording from the ventromedialprefrontal cortex (vmPFC). Stimulation may be performed with subcorticalDBS leads placed in the nucleus accumbens (NAcc) and ventralcapsule/ventral striatum, for example. One of more of the electrodes 16may be a recording or stimulating electrode in this configuration.Additionally, or alternatively, any of the electrodes 16 may be replacedwith one of several commercial designs capable of recording singleunits.

Algorithms stored on the hub 12 may enable the system 10 to merge spikeand field-potential data to estimate the patient's psychiatric state anddeliver therapeutic stimulation. The frequency of stimulation deliverymay depend upon how frequently neural signatures that triggerstimulation occur. Those signatures may be fully or partly under apatient's direct intentional control. Real-time telemetry may enable aclinician, for example, to tune algorithm parameters as required by thepatient. Thus, the system 10 will also provide neuroscientists anunprecedented view of real-time brain activity in fully consciouspatients interacting with real-world environments.

The system 10 may be configured to operate in one or more modes. Forexample, in autonomous mode, the hub 12 may be controlled by an internalprocessor 26 and powered by an internal battery 28 that can be rechargedperiodically, as shown in FIG. 2. Low-bandwidth (e.g., 2MBps) telemetry30 may be used to report on the hub's state of health and provide thesubject comfort in the ability to both wake up and put the system 10 tosleep when needed. In a continuous recharge mode, the head-mountedinterface 20 can be attached to the base station 18 to wirelessly powerthe implanted device via a charger 32. The continuous recharge mode maybe desirable when operating the system 10 in modes that consume morepower. In a base station control mode, a high-bandwidth telemetry link34 can be used to stream live neural data to be processed within thebase station 18, which can then control stimulation therapies over thelow-bandwidth telemetry 30. The base station control mode permitshigher-power algorithms to be implemented without burden on the hub 12and satellite modules 14. In a computer control mode, the offlineprocessor 22 can be connected through the base station 18 to furtherincrease processing power and provide additional interfaces andresources to researchers and clinicians. The computer control mode maybe used during the initial configuration period, where the implanteddevice may be running sub-optimally with high-channel counts and highprocessing power. As the device and algorithms are iteratively matchedto the patients needs, operation may become less dependent upon externalmodes and move toward autonomous control.

With continued reference to FIG. 2, each satellite module 14 mayinterface with or be fully integrated into an electrode 16. In oneexample, the electrodes 16 may be multi-channel macro-electrodes ormicro-electrodes. A cross-point switch (CPS) matrix (Neural CPS) 36 maybe used to re-configure the electrodes 16 for recording and stimulation.Additionally, the neural CPS 36 may enable multiple electrodes 16 to besimultaneously connected to one of two analog stimulus inputs to createlower impedance larger-area electrode clusters for stimulation. Neuralamplification and digitization within the satellite module 14 mayprovide a higher level of signal to noise ratio (SNR) and reduce thewire-count burden to communicate with the hub 12 through multiplexeddata. A field-programmable gate array (FPGA) 38 may reduce wire-countsand provide control over the Neural CPS 36 and amplifier. The FPGA 38may also provide charge-balanced communication with the hub 12, whichmitigates risks of tissue damage caused by DC leakage currents. Powermay be provided by an AC power supply 40 that will convert an AC supply42 provided by the hub 12 into DC voltages required by the satellitemodules 16.

As previously described, the system 10 may be capable of recordingseveral types of neural signals (e.g., Spikes, LFPs, ECoG, etc.) fromdifferent types of electrodes 16 (e.g., Micro-, DBS, ECoG arrays, etc.).This requires the satellite 14 to include a neural amplifier 44, such asa multi-purpose re-configurable neural amplifier that is both low-noiseand low-power. Low noise may be necessary to capture small LFP and ECoGfeatures in high gamma frequency bands, while low power may be necessaryto reduce heat dissipation and extend the operational lifetime.

Each satellite module 14 may include a unique ID 46 that is linked toits specifications, internal components, manufacturing data, andattached electrodes 16. The satellite's 14 unique ID 46 may allow thehub 12 to interrogate the connected satellite modules 14 and configureitself based upon returned information. This information may then betransmitted to the base station 18 to be verified and logged. Thesatellite module 14 may also be configured to monitor and report a setof health parameters that includes electrode impedance and may alsoinclude temperature, humidity, supply voltage, and hub-to-satellite leadline integrity, for example. In the case of malfunction, actions may betaken by the hub 12 to modify or shut-down satellite functions.

Turning now to FIG. 3, an exploded view of one of the satellite modules14 of FIG. 2 is shown. The components of the satellite module 14 may beenclosed within a hermetically sealed housing 48 that is hard-wired tothe electrodes 16 and a lead body with a distal connector forinter-operative attachment to the hub 12 (see FIG. 2). The housing 48may be a titanium package measuring about 14 mm in diameter and about 7mm in thickness. This package size is targeted so that the satellitemodule 14 can fit within a standard neurosurgical craniectomy window. Alid 50 made from a ceramic material, for example, may include aplurality of hermetic apertures 52 and may be coupled to the housing 48to form a hermetic seal. Electronics of the satellite modules 14 may bepositioned on two rigid flex boards 55 in stacked configuration. In someembodiments, wires to the electrode arrays 16 and lead cables to the hub12 may be permanently attached to the satellite module 14.

Returning to FIG. 2, the hub 12 may configure the satellite modules 14,process neural data, decode neuropsychiatric states, control closed-loopneurostimulation therapies, and transmit data to the external basestation 18. The hub 12 may include the processor 26, which may be alow-power processing core configured to execute algorithms. The hub 12may further include one or more current pulse generators 54 for applyingstimulation therapies through the electrodes 16. In addition, the hub 12may include a memory 56, such as dynamic random-access memory,accessible by the processor 26 for stored data. The hub 12 may alsoinclude the high-bandwidth telemetry link 34 for neural dataexfiltration, and the battery 28, which may be wirelessly charged, forextended operational life.

The current pulse generators 54 for neural stimulation may be positionedin the hub 12 and may be responsible for generating the programmablecurrent-controlled stimulation pulses for neuromodulation. Analogstimulation waveforms may be sent to the satellite modules 14 and routedto stimulation electrodes 16 via the neural CPS 36. In order toeffectively modulate stimulation therapies based upon closed-loop neuralactivity, stimulus waveforms may be dynamically re-programmable by hubalgorithms. Waveforms may be biphasic and charge balanced, and voltagesacross the electrode-tissue interface may be limited to the water windowto inhibit chronic tissue damage. For monopolar stimulation, currentsmay be returned to the conductive hub 12, and for bipolar stimulation,currents may return through adjacent electrodes 16.

With continued reference to FIG. 2, the hub 12 may further include oneor more sensors 58 including, for example, humidity, temperature, andaccelerometer sensors. Humidity and temperature sensors may be a usefulpart of health monitoring in the hub 12. For example, an increase inmoisture may be detected by the sensor 58 and a warning of failure dueto ingress of moisture from the body, or possible accumulation ofmoisture from parts may be provided. In another non-limiting example,accelerometer data gathered from one of the sensors 58 may be useful fordetermining subject activity, including sleep, which may be used as asignal for closed-loop control. Measurements acquired from the sensors58 may be logged and reported to the base station 18. Similar to thesatellite modules 14, the hub 12 may include a unique ID, for example a16 bit unique identifier, that can be read through the low-bandwidthtelemetry 30.

As previously described, the hub 12 may include the processor 26 and acontrol unit 62, such as a programmable logic controller (PLC) to managesystem-level functions and execute closed-loop algorithms for adaptiveneuromodulation therapy. The processor 26 and control unit 62 may beadaptable and re-programmable in order for closed-loop algorithms to bedeveloped, tested, and tuned for enhanced therapeutic benefit to eachpatient. The processor 26 and control unit 62 may be capable ofconfiguring satellite modules 14 and receiving neural data, extractingsignal features from raw neural data, decoding neuropsychiatric states,modulating stimulation therapy, monitoring and logging system healthdata, detecting and recording neural data, and managing wirelesscommunication to the base station 18.

The hub 12 may further include a nonvolatile storage module 64. Anon-limiting example would be a single-level cell (SLC) flash. SLC flashmay have favorable power characteristics and sufficient bandwidth forhigh channel count raw spike recording. Peak power consumption is below30 milliwatts, with spike scenarios expected around 17 milliwatts. Asrequisite data rate decreases (fewer channels recorded or LFPs vs.spikes or features vs. raw), power consumption decreases. Recording 320channels of LFP (Fs=2000 Hz) is expected to take less than 2 milliwatts.These numbers assume a pre-erased device and page-size writes.

Collection of data may be necessary to understanding brain function inrelation to neuropsychiatric diseases and in assessing the effectivenessof the device in treating symptoms. In addition to being able to streamdata via the high-bandwidth telemetry link 34, data may be storedinternally and later uploaded to the base station 18. Depending upon thetype of data desired, varying lengths of recordings may be saved. Forexample, storing only spike times requires less memory than storing rawspike data.

The hub 12 may further include a wireless power link 60 for inductiverecharge. Thus, the hub 12 may include three antennas for inductiverecharge (wireless power link 60), high-bandwidth telemetry 34, andlow-bandwidth telemetry 30. As shown in FIGS. 4A and 4B the antennas 30,34, 60 may be constructed from PCB micro strip traces, for example, andpositioned on an antenna board 66 near the top of a housing 68 where theantennas can be in close proximity to the scalp. A magnet 70 may bepositioned in the center of the antenna board 66 to help with alignmentwith the head mounted interface 20.

A connector 72, such as a high-density connector that may include 64 ormore sockets, may be utilized for connection to the satellite modules 14and may also be connected to the hub housing 68. The connector 72, thebattery 28 and the hub housing 68 may be attached by a thin film flexcable 74 and over-molded with medical-grade silicone (not shown), forexample. The thinness of the hub 12 components may be desirable forcomfortable and unobtrusive implantation on top of the skull, and theflexibility of the hub 12 components may provide a better fit to varyingskull curvature. As shown in FIG. 4C, the battery 28 may be contained ina package 76, such as a hermetically-sealed titanium package, and maypower the hub 12 components via a multi-layer flex cable, for example.The housing 68 of the hub 12 components may be constructed of lowtemperature co-fired ceramic (LTCC) which may allow improved performanceof the wireless power link 60, high-bandwidth telemetry 34, andlow-bandwidth telemetry 30.

Returning to FIG. 2, the base station 18 is coupled to the processor 22and user interface 24. The user interface 24 may be a display including,for example, LEDs and buttons for basic functions, such as putting thesystem 10 to sleep, waking the system 10 up, performing a check status,and the like. The base station 18 may also be coupled to the headmounted interface 20 that includes antennas for the high-bandwidthtelemetry 34 and the wireless power charger 32. Without the head mountedinterface 20 attached, the base station 18 may communicate and controlthe implanted unit via the low-bandwidth telemetry 34. When the headmounted interface 20 is attached, the high-bandwidth telemetry 34 linkmay be used to stream real-time neural data to the base station 18 whereclosed-loop algorithms can be executed at higher power than can be usedinside the implant. Closed-loop control may then be achieved bycontrolling the system 10 over the low-bandwidth telemetry 34 link. Thisclosed-loop control may include a component of directlydetecting/decoding the patient's intentions regarding neurostimulation.The head mounted interface 20 may also be used to wirelessly recharge orcontinuously power the implant via the charger 32.

As shown in FIG. 5, the head mounted interface 20 may include a firstantenna 78 for the wireless power charger 32, a second antenna 80, suchas a transmit antenna, for the high-bandwidth telemetry 34, and a thirdantenna 82, such as an receive antenna, for the high-bandwidth telemetry34. The antennas 78, 80, 82 may be mounted on a PCB 84 which alsoincludes matching networks 86, a wireless power amplifier 88, and analignment magnet 90. The alignment magnet 90 may help to align andretain the head mounted interface 20 with the magnet 70 of the hub 12 toensure power transfer efficiency and communication. The head mountedinterface 20 may further include a cable bundle 92 for connection to thebase station 18 that may be soldered directly to the PCB 84, forexample. In one non-limiting example, the cable bundle 92 may be a 1 mhighly flexible cable bundle including two RF coax cables, as well as DCpower and clock signals for the wireless power transmitter. A plasticenclosure (not shown) may protect the PCB 84 from contact by the user.The head module may connect to the base station via a like 1 m highlyflexible cable bundle.

In some embodiments, the system 10 may be a non-invasive system forstimulating the brain. That non-invasive realization may collapsecomponents such as the satellites 14, base station 18, and hub 12 into asingle part. The non-invasive system may enable not only treatment viainduction of brain plasticity, but also pre surgical planning of DBStargets. The non-invasive system may be combined with EEG, for example,to produce a closed-loop system. The non-invasive system may include aplurality of scalp or non-contact electrodes and/or neuro-stimulationcoils in communication with a software-controlled helmet, cap, or set ofelectrodes that can stimulate areas of the brain. The embedded softwaremay steer the amount and polarity of energy sent to each electrode, thusshaping the E-fields and allowing accurate targeting of specificcortical areas. In other embodiments, the non-invasive system may becombined with non-electrical modalities, such as magnetic or ultrasonicstimulation, for stimulating the brain.

Referring now to FIG. 6, a flow chart sets forth exemplary steps 200 fordiagnosing and treating mental and emotional disorders by analyzing anindividuals brain with both imaging and behavioral testing. The methodincorporates the affective BCI component in the closed loop,symptom-responsive psychiatric DBS system 10 previously described. Ingeneral, the method may include performing a series of brain scans on apatient while the patient performs a battery of behavioral tasks. Fromthe patient's performance on the task battery and/or the brain'sactivations during performance of the tasks, the system 10 may identifywhat is abnormal for that individual patient in terms of functionaldomains (e.g., fear, reward motivation, emotion regulation, decisionmaking/impulsivity, attention/preservation, cognition, etc.) as shown inthe table of FIG. 7. The system 10 then may link the patient-specificbehavioral measurement to patterns of activation and de-activationacross different brain regions, identifying specific structures that arethe source of the patient's individual impairment. The system 10 maythen use a range of invasive or non-invasive brain stimulationtechnologies to specifically target the brain regions and provideindividualized psychiatric treatment, or to probe the network further tobetter classify the impairment.

Thus, the method to be described shifts away from treating patientsbased on conventional classifications of mental diseases and disorders(e.g., The International Classification of Diseases (ICD) or theDiagnostic and Statistical Manual of Mental Disorders (DSM)).Diagnosis-based treatment is the standard of care in psychiatry.However, there is a widespread recognition that the diagnoses do notcorrelate well to underlying brain circuit dysfunction. For example, asingle disorder called “major depression” could be one or more differentkinds of brain dysfunction. The NIH has proposed a domain orientedsolution, the Research Domain Criteria (RDoC). However, RDoC is meant asa scientific research tool, not a clinical system or diagnostic tool.Thus, the present system and method enable a shift to functional-domaindiagnosis using behavioral testing that consider more abnormal domainsas the “problem” to be treated.

Returning to FIG. 6, the method may begin by administering atransdiagnostic assessment at process block 202. Administering thetransdiagnostic assessment requires a patient to perform one or morepsycho-physical tasks to determine the patient's impairment along a setof functional domains. The functional domains, as shown in the table ofFIG. 7, may be validated psychological constructs that aretransdiagnostic and occur in multiple named mental disorders (e.g.,PSTD, MDD, generalized anxiety disorder (GAD), traumatic brain injury(TBI), substance use disorder (SUD), borderline personality disorder(BPD), pain disorder, etc.). The disorders shown in bold represent theprimary domains for the disorder.

The functional domains are not limited to those shown in FIG. 7, and inone non-limiting example, the system 10 may be configured to access theNIMH's RDoC Matrix for additional functional domains. Each functionaldomain may be measured by one or more corresponding behavioral tasksthat probe each domain and certain brain regions known to be involved inperformance/impairment for each domain. The various brain regions mayinclude, but are not limited to the rostral anterior cingulate (rACC),dorsolateral prefrontal cortex (dIPFC), ventromedial prefrontal cortex(vmPFC), orbitofrontal cortex (OFC), subgenual anterior cingulate(cg25), amygdala, anterior insula, thalamus, periaqueductal gray,ventral striatum, subthalamic nucleus, hippocampus, dorsal striatum, andthe like.

The transdiagnostic assessment, optionally administered at process block202 of FIG. 6, may be performed by the patient on a computer, forexample, and may be one or more simple computer games that are eachdesigned to probe functioning in a different domain. As a non-limitingexample, the tasks outlined below will be described with respect to apatient who has a difficulty with fear regulation (a subset ofdifficulties with emotion regulation) which may commonly be found in apatient who has PTSD, but can also be seen in obsessive-compulsivedisorder, generalized anxiety disorder, panic disorder, and sometimesmajor depressive disorder. Such a patient may have several psychiatricdiagnoses, for example, a prototypical patient may be a returningservice member who had been diagnosed with PTSD, but also with analcohol use disorder and sub-threshold depressive symptoms. All of thebelow descriptions of task-based diagnostics and resulting stimulationprotocols may be combined with the patient-intention controllerdescribed in below to form a hybrid brain-computer interface.

Turning to FIG. 8A, a fear learning and extinction task is shown formeasuring the functional domain of fear extinction. Performance on thistask assesses integrity of the fear and fear suppression systems. Thefear learning and extinction task may be impaired for the example PTSDpatient described above. During fear conditioning, the patient may bepresented with a conditioned stimulus 302, such as multiple, differentcolored lights. Two of the lights, for example, may be paired with thepresence of an unpleasant, but not painful electrical current applied tothe fingers of the subject, which is called an unconditioned stimulus304. This pairing induces conditioned fear responses, such as increasedheart rate (HR) and skin conductance response to the presentation of theconditioned stimulus 302. In some embodiments, the fear responses may bemeasured through biomarkers. The conditioning phase happens outside theimaging scanner before an initial scan.

During an extinction phase, which happens in the imaging scanner, thepatient may be presented with the conditioned stimulus 302 in theabsence of one or more of the unconditioned stimuli 304 (i.e., one ofthe lights is “extinguished”—repeatedly re-presented without the shock,so the patient learns it is now safe.), which leads to abolishment ofthe conditioned responses. The degree of extinction recall (i.e., howmuch the extinction has been consolidated) is then assessed by arepetition of the extinction trials in the scanner on a second scanningday. For example, during the recall phase, the patient may be presentedwith both the feared and safe lights. Patients with deficits in fearextinction learning either are unable to learn that one of the lights issafe on Day 1, or will learn this but not recall it on Day 2. A patientwith difficulty learning safety may show elevated fear biomarkers (e.g.,heart rate, heart rate variability, eye pupil diameter, skin conductancedue to sweating, etc.) compared to normal controls at multiple phases ofthe task.

As shown in the graph of FIG. 8B, the PTSD subject shows impaired recallon Day 2 compared to a population norm. In other disorders, such as MDD,a patient may exhibit extinction deficits (threat bias). Similar to PTSDpatients, patients with GAD may show impaired recall, while TBI patientsmay exhibit impaired extinction learning. SUD patients may exhibitimpaired fear acquisition (reward bias), and BPD patients may exhibitrapid acquisition, impaired extinction, and maybe impaired recall, whilepain patients may exhibit impaired extinction and recall.

Turning to FIG. 9, an example aversion reward conflict (ARC) task isshown for measuring the functional domain of reward motivation.Performance on this task assesses whether the patient will accept acertain amount of punishment to obtain a certain amount of reward, aswell as the patient's reaction time. The ARC task may be impaired forthe example PTSD patient described above.

During the ARC task, the patient may be trained on a simple two-choicediscrimination task. The patient's performance on each trial may bemodified by the expectation of both rewards (e.g., monetary rewards) andaversive stimuli. In the ARC paradigm, the task begins with the patientfixating on a central point 402 on a computer monitor, a shown in FIG.9. Next, two cues 404 represented as circles are presented on theperiphery of the computer monitor. One cue is the combined option cuethat represents two distinct stimuli: the probability of aversion and anexpected reward of varying magnitude. The probability of aversion isindicated by a color of the cue (e.g., Blue: low, 10%; Black: medium,50%; Red: high, 90%) and consists of a half second shock to thefingertips. Additionally, or alternatively, the unpleasant punishmentmay be an air puff to eye or a loud noise, for example. For reward, themagnitude is determined by a thickness of the cue (e.g., Thin: small;Medium: medium; Thick: large). In some embodiments, the reward isrepresented by pictures of money (e.g., small ($0.10), medium ($0.25),and large ($1.00)). The other cue may be a white “pass” circle thatpredicts a smaller reward ($0.01) as a safe alternative with noaversion. The subjects must choose either the left hand or right handcue 404 by pressing the corresponding button on a button box, forexample. The side of the screen in which the cues 404 are presented ineach trial may be randomized.

Thus, the ARC task can measure how often a patient takes the risk choiceoverall, for example. In addition, the ARC task can measure what levelof reward, and what level of risk-reward balance, is needed to get thepatient to run the risk of punishment, as well as the patient's reaction(i.e., how long does the patient take to choose). The ARC task canfurther determine how the patient's choice may change immediately afterthe patient “bet wrong” and received punishment.

In one example, the system 10 may incorporate DBS to modify ARCbehavior. For example, the NAcc brain region may be stimulated toincrease reward sensitivity, the STN brain region may be stimulated todecrease impulsivity, and the Amygdala brain region may be stimulated toincrease or decrease threat sensitivity.

The ARC task may show that the example PTSD patient is more driven bythreat than reward. In other disorders, such as MDD, the ARC task mayshow decreased reward sensitivity, possibly with increased threatsensitivity (dissociable). Similar to PTSD, the ARC task may show that aGAD patient and/or a BPD patient is more driven by treat than reward.TBI patients may show faster response speeds and insensitivity topunishment from the ARC task. A SUD patient may be more driven by rewardthan threat, and a pain patient may be more driven by threat than rewardand show an overall lower aversion threshold.

Turning now to FIG. 10, an emotion conflict resolution (ECR) task isshown for measuring the functional domain of emotion regulation.Performance on this task assesses a patient's reaction time, as ameasure of conflict. The ECR task may be impaired for the example PSTDpatient described above. During the ECR task, the patient may be shownone or more images 502 of a person showing an emotion. A word 504describing an emotion is displayed over the image 502. The patient isasked to report the emotion shown in the image 502 while attempting toignore the word 504 by pressing a 1 (fearful) or 2 (happy) key on abutton box, for example. Trials are either congruent (facial expressionand word match; e.g., happy expression and the word “HAPPY”), orincongruent (facial expression and word do not match, e.g. happyexpression and the word “FEAR”). The task is designed to measure theability to regulate emotions and adapt behavior after being presentedwith the initial “surprise” of incongruent stimuli. The presentation ofan incongruent image after seeing a congruent image encodes the initialrecognition of emotion conflict (“low conflict resolution”). Thepresentation of an incongruent image followed by a second incongruentimage encodes the ability to regulate the emotion conflict and continueperforming the task, or behavioral adaptation (“high conflictresolution”).

Thus, the ECR task can generate affective responses, while also evokingtop-down processing and stressing that network. In addition, the ECRtask can identify both cognitive conflict and the response of the brainto highly salient affective stimuli to understand the interaction (i.e.,how does a patient become differentially biased to process emotions whenunder load?) and the dissociation (i.e., how does X get processed when Yis controlled for?). The ECR task can also measure how slowed down apatient is by conflicting emotions, or by fearful instead of happy facesshown in the images 502, for example. The ECR task may also identifywhether seeing an image 502 with a fearful emotion makes the patientmore error-prone.

The ECR task may show that the example PTSD patient, GAD patient and/orBPD adapts more slowly to conflict when emotion distracts. The ECR taskmay show that a MDD patient has faster response time on negative-affectstimuli and more difficulty with incongruent images 502 and words 504.TBI and SUD patients may show primarily incongruence without affectivecomponent (except from comorbidities). A pain patient may show similarresults as a GAD patient. In one non-limiting example, the ECR task mayshow abnormal behavior in a patient with depression when compared tocontrols. That patient would be expected to also endorse greaterdifficulty with regulation emotions using a standardized clinical ratingscale.

Further, the patient with depression, for example, may exhibit greaterrecruitment relative to the healthy controls of brain structuresidentified as key regions for processing negative affect and salience;specifically, the amygdala, insula, and rostral anterior cingulate(rACC). By contrast, the healthy controls may show greater recruitmentof structures identified as key regions for the regulation of emotionand attentional control; specifically, the dorsal anterior cingulate(dACC) and the dorsolateral prefrontal cortex (DLPFC).

Patterns of functional connectivity between brain regions showingdifferential activation in the patient with depression and healthycontrols and other key regions subserving emotion processing andcognitive control of emotion may be analyzed. This analysis may furtheridentify where and how the individual patient is deviating from healthyindividuals along distributed neural networks subserving theseprocesses.

Healthy controls may demonstrate strong cohesion in activation betweenthe amygdala and cognitive control regions (DLPFC, dACC, rACC). Bycontrast, the patient with depression and ADHD may demonstrate lowcohesion between the amygdala and cognitive control regions. Inparticular, lower amygdala-rACC functional connectivity may be foundrelative to healthy controls. Additionally, healthy controls maydemonstrate strong functional connectivity between the amygdala and dACCduring High Conflict Resolution (emotion regulation) relative to LowConflict Resolution (emotion conflict recognition). In contrast, thepatient with depression and ADHD may show a reduction in cohesionbetween amygdala and dACC during High Conflict Resolution. Thus,specific altered connectivity in a known network of emotion regulation,one that is linked both to the specific task and to the generaldiagnosis (depression) can be identified in the individual patient.

The above described analyses may reveal strong correlations between thestrength of functional connectivity between the rACC and dACC and 1)reaction time (behavior); 2) emotion regulation measures (psychologicalfunctioning); and 3) psychiatric symptoms and impairment. Thosesignatures may differ substantially between individual patients.Therefore, a specific brain signature, at the slow metabolic time coursedetectable with fMRI, that has clinical implications may be identified.

In one example, the system 10 may incorporate DBS to modify ECRbehavior. For example, the dACC, VC/VS and dIPFC brain regions may bestimulated to increase the patient's ability to process conflict.Additionally, or alternatively, the Amygdala brain region may bestimulated to lower threat bias and affective activation to reducedistractions.

Turning now to FIG. 11, a gambling task is shown for measuring thefunctional domain of impulsivity and decision making. Performance onthis task assesses an overall percentage of time the patient bets theircard, as well as reaction times and responses to losses. For example, a“rational” person will typically bet their card around 50% overall andshow specific patterns on specific cards. If the patient bets more orless than 50% overall the system may identify risk/reward problems withthe patient.

During the gambling task, the patient may be presented with a reduced5-card deck of playing cards. The patient is instructed to play “war”against the computer and the patient bets whether their card is higherthan the card displayed by the computer. There is an optimal bet foreach card except 6. The 6 card, by definition, is a 50% win-lose chance,“decisional equipoise”. As shown in FIG. 11B, the patients reaction timeis greatest for the 6 card.

The gambling task may show that the example PTSD patient is unimpairedand/or impulsive. In other disorders, such as MDD, the gambling task mayshow loss aversion and generally slowed reaction times. GAD patients mayshow loss aversion and increased conservatism after a loss on a priortrial. TBI patients may show impulsivity and overall broad activations,and SUD patients may show impulsivity. BPD patients may show impulsive,suboptimal decision making, and pain patients may show a strong lossaversion, similar to MDD patients.

In one example, the system 10 may incorporate DBS to modify impulsivebehavior, as indexed by the gambling task. For example, as shown in thegraph of FIG. 11C, the STN brain region may be stimulated while thepatient is betting, and the result shows a more conservative bet incomparison to no stimulation or fixed stimulation. The NAcc, VC-VS,and/or the OFC brain regions may also be stimulated to increaseimpulsivity. The subcortical brain region may also be stimulated toalter the impulsive-conservative balance. Additionally, oralternatively, the dACC brain region may be stimulated to slowprocessing, thereby forcing the patient to slow down and think beforebetting.

Turning now to FIG. 12A, a multi-source interference task is shown formeasuring the functional domain of flexibility and perseveration.Performance on this task assesses reaction time and measures cognitivecapacity and cognitive flexibility. The multi-source interference taskmay be impaired for the example PTSD patient described above.

During the multi-source interference task, the patient may be provided agroup of numbers 602 and asked to select one number from the group ofnumbers 602 that is different from the others. The patient may be askedto perform the multi-source interference task as quickly and accuratelyas possible. In general, interference stimuli should increase thepatient's reaction time and switching rapidly betweeninterference/non-interference trials may impose extra load. Further,having the patient perform the multi-source interference task alongsideother emotional tasks, allows the system 10 to dissociate out thedifferent effects and their networks.

The multi-source interference task may show that reaction times for theexample PTSD patient is slowed under load. In other disorders, such asMDD and pain, the multi-source interference task may show greaterinterference effects and/or wider activation under interference. Similarto PTSD, a GAD patient and BPD patient may show slowed reaction timesunder load. TBI patients, and some SUD patients, may show poortrial-trial adaptation and greater interference effects due to dACCand/or dIPFC impairment, for example.

In one example, the system 10 may incorporate DBS to modify attentionand perseveration behavior. As shown in FIG. 12B, the VC/VS brain regionis chronically stimulated, but the DBS is turned off while the patientperforms a multi-source interference task. As shown in the graph, thepatient's reaction time is less when DBS is on, whereas the reactiontime is longer under cognitive load from interference. That is,effective tuning of the DBS may modify the patient's reaction time,representing improvements in attention and mental flexibility. Thesystem 10 may monitor responses in the dIPFC and dACC brain regions andtune VC-VS stimulation to keep overall activation adequate. In addition,frequency-domain signatures may be uncovered in processing of acquireddata and serve as calibration to dissociate non-affect signals thatshould not be closed-loop targets.

Turning now to FIG. 13A, an associative learning task is shown formeasuring the functional domain of cognitive capacity. Performance onthis task assesses the patient's speed to learn new items, memory,number of items that the patient can perform correctly at some criterion(e.g., 80% consistent correct), speed to detect a reversal when ithappens (i.e., to notice that what was correct is now wrong), andperseverative thinking. The associative learning task may be impairedfor the example PTSD patient described above.

During the associative learning task, the patient may be presented witha stimuli 702, such as a colored shape, and be instructed to move ajoystick (not shown) to the right, for example, when presented with thestimuli. Thus, when the patient sees the stimuli 702, he/she shouldlearn stimulus-response associations. The speed of learning associationsand the number of associations that the patient can learn correctly maybe a measure of the patient's overall cognitive capacity. Overallcognitive capacity may also be measured by adding rule-switching orreversals into the associative learning task. For example, after thepatient learns a rule associated with the stimulus, the rule maysuddenly change. Rule-switching or reversals may also be a measure ofcognitive flexibility, top-down attention shifting, and frustrationtolerance, which is often impaired in psychiatric disorders. Theassociative learning task also provides situations with prepotent andoverlearned responses, the inhibition of which is a core psychiatricfunction.

The associative learning task may show that the example PTSD patient ischaracterized by extinction/reversal specific deficits. MDD patients mayshow more difficulty with set shifting, even when cued. Similar to PTSD,the associative learning task may show that a GAD patient showsextinction/reversal specific deficits. TBI patients may showperseveration on uncued or cued reversals and may also be impaired onassociation generally. SUD patients may show possible general impairmentduring the associative learning task, and BPD may show extinctions,reversals, and decreased capacity overall. Pain patients may showimpaired set-shifting and reversals during the associative learningtask.

In one example, the system 10 may incorporate DBS to modify associativelearning behavior. For example, the NAcc, caudate, STN, OFC and/or thehippocampus brain region may be stimulated to modify the patient'sability to learn. Stimulation may be performed during a specifictime-limited formal therapy or exposure sessions, and biomarkers (e.g.,heart rate, skin conductance, etc.) may be incorporated to enhancesafety/extinction learning.

Turning now to FIG. 13B, cumulative data is shown from over 20 sessionsin animals performing an associative learning task utilizing DBSstimulation in the Cd and NAc brain regions. Closed-loop stimulation maybe applied in different combinations. For example, the best performanceis shown when the subject received combined stimulation of the NAc andCd brain regions. Thus, as shown in the graph of FIG. 13B, DBSstimulation at correct sites and timing may enhance learning and showingan improvement in overall performance approaching 100% correct.

Returning to FIG. 6, one or more of the psycho-physical tasks justdescribed may be given to the patient while administering thetransdiagnostic assessment at process block 202 in order to determinethe patient's impairment along a set of functional domains. Once thetransdiagnostic assessment is administered, the system 10 may recordelectrical, magnetic, or other physiologically produced activity fromthe patient's brain and/or body at process block 204 while the patientis performing one or more of the psycho-physical tasks. The activity maybe recorded through non-invasive or invasive methodologies.

More specifically, at process block 204, while the patient is performingthe task(s) of transdiagnostic assessment, multiple forms ofelectro-magnetic signals may be recorded from the patient during asingle session, for example, or during multiple sessions of multiplemodalities (i.e., imaging types) done over several days. In someembodiments, the majority of the electro-magnetic signals are measuredfrom the patient's brain using one or more of structural magneticresonance imaging (MRI), with particular variants such as diffusiontensor imaging (DTI), functional magnetic resonance imaging (fMRI),positron emission tomography (PET), single-photon emission computedtomography (SPECT), electroencephalography (EEG), magnetoencephalography(MEG), near-infrared spectroscopy (N IRS), reflected ultrasonic energy,fluorescent energy emitted from molecules within certain structures,direct recording of the brain with invasive, surgically implantedelectrodes, and the like. Alternatively, any suitable method orcombination of methods for recording the electro-magnetic signals of thebrain with sufficient spatial resolution and temporal resolution may beused.

In some embodiments, the electrical activity recorded from the body atprocess block 204 may be accomplished by recording one or morebiomarkers. These may be signals that relate to brain activity andpsychological state, but are not directly measured from the brain. Forexample, the biomarkers may include, but are not limited to, heart rate,eye movements and blinks, eye pupil diameter, skin conductance/galvanicskin response (i.e., measure of autonomic arousal), respiratory rate,recorded speech (e.g., quantitatively analyzed for tone, amount, andprosody), and electromyography.

Once activity from the brain and body are recorded at process block 204,optionally, the system 10 may identify the patient's deviation infunctional domains, such as the functional domains shown in the table ofFIG. 7, that the transdiagnostic assessment showed as compared tohealthy controls or population norms at process block 206.Transdiagnostic assessments of population norms may be acquired from adatabase of patients without evident psychiatric impairment who haveperformed the various transdiagnostic tasks. The patient's performanceon the battery of transdiagnostic tasks may be compared to healthycontrols by the system 10, or alternatively a trained clinician. Aspreviously described, performance varies from task to task and mayinclude, for example, how a biomarker changed in response to certainstimuli, how fast the patient responded to questions, what decisions thepatient made when confronted with uncertain choices, and the like.

In one non-limiting example, the example PTSD patient may showimpairment in the functional domains of fear extinction and a partialdeficit in emotion regulation. However, the patient may not showimpairment in the functional domains of cognitive capacity or rewardmotivation, whereas the patient may or may not show impairment in thedecision making/impulsivity functional domain.

As shown in FIG. 14, analysis and interpretation of a transdiagnosticassessment may classify patients by a distance from an origin 802. Theorigin 802 represents the population mean of healthy controls in atransdiagnostic space 804, which generally is high-dimensional. Scalingof the transdiagnostic space may be spherical, normalized by Z-scores,or allowed to remain elliptical. The degree of impairment along eachaxis 806 (i.e., the distance from the origin 802) may then prioritizethe patient's treatment in terms of selecting brain stimulationlocations and treatment modalities, as will be described in furtherdetail below.

The example PTSD patient, shown in FIG. 14 as Patient A, may have aphenotype characterized by problems with excessive anxiety and deficientsafety learning and is impaired in fear extinction and emotionregulation in the transdiagnostic space. However, Patient A isessentially at population normal for reward motivation. An alternatepatient, Patient B shown in FIG. 14, may have depressive symptoms butless anxiety and shows over-regulated (i.e., flattened) emotionregulation and low reward motivation (i.e., anhedonia). However, PatientB may have no meaningful defect in fear extinction. Therefore, Patient Aand Patient B can be classified differently in the transdiagnostic space804.

Returning to FIG. 6, once the patient's deviation(s) in functionaldomains is identified at process block 206, the system 10 may identifythe brain regions and signal characteristics within those brain regionsthat correspond to the patient's deviation from functional domains atprocess block 208. More specifically, once the patient's functionaldomains of maximal impairment have been identified, brain regions, aswell as specific sub-regions within the brain regions, may be identifiedthat correlate to that abnormality. This is done using the recordings ofblocks 210 and 212, but may additionally involve consideration ofgeneral patterns of brain activation known to exist in the scientificliterature.

In order to identify the brain regions and signal characteristics atprocess block 208, the brain imaging data acquired during thetransdiagnostic assessment at process block 202 may be analyzed withrelation to the patient's behavior on individual trials (stimuluspresentations) of each behavioral task, starting with the most-impairedfunctional domains. The imaging data from the impaired functionaldomains may be compared to population averages to identify the brainregions where the patient has abnormally high or low levels of brainactivity. Alternately, the population averages of other patients withsimilar behavioral performance may be substituted as a proxy for theindividual patient's brain activity.

Furthermore, because the brain imaging data, such as brain imaging data902 shown in FIG. 15, includes the patient's specific brain anatomy, thesystem 10 may identify one or more points 904 within the individualpatient's brain that are generating the abnormal activity, as shown inFIG. 15. The point 904 of abnormal activity may be compared to a point906 of normal activity as determined by behavior on a transdiagnostictask identified by a population average. In the example shown in FIG.15, the individual patient with impairment on the task had a focus ofmaximum activation that was shifted by about 1 centimeter from thepopulation average.

Once the brain regions related to the patient's impairment (i.e.,deviations in functional domains) are identified at process block 208,additional imaging data of the brain regions may be obtained at processblock 210 in order to identify abnormal features of neural activity. Theadditional imaging data may be acquired using any imaging modalityhaving suitable high time resolution including, for example, EEG, MEG,invasive recording, EMG, and the like. Abnormal features of neuralactivity may include, for example, electrical signals from event-relatedpotentials related to features of a transdiagnostic task, event-relatedsynchronizations and de-synchronizations in the frequency domain relatedto task features, changes in phase of oscillatory brain activity (e.g.,phase resetting), coupling and connectivity between different brainregions (e.g., coherence, phase-locking values, Granger causality,etc.), interactions of functional brain rhythms (cross-frequencycoupling) within and between different structures, and the like.

Once the patient's impaired functional domain(s) have been identifiedand the brain regions and signals correlated to the impairment have beenidentified, the system 10 may apply stimulation to the identified brainregions at process block 212. In one example, stimulation may beperformed with the electrodes 16 (see FIG. 1) having cortical and/orsubcortical leads placed near brain regions of the patient. Thestimulation may be applied to the brain regions identified at processblock 208 in order to alter activity in those regions.

In some embodiments, sub-regions are selected within each brain regionbased on the imaging data obtained at process block 210. Stimulationmodalities for applying stimulation to the identified brain regions andsub-regions at process block 212 may include, but are not limited to,non-invasive electro-magnetic modalities (e.g., transcranial magneticstimulation, transcranial direct- or alternative-current stimulation,transcranial focused ultrasound, infrared/optical through-skullmodulation, etc.), invasive electro-magnetic modalities, and invasiveoptical modalities. In the case of invasive electro-magnetic modalities,electrodes or other amplifying devices may be surgically implanted intoone or more brain regions and/or sub-regions. In the case of invasiveoptical modalities, transfection of one or more brain regions withproteins or other molecules may be involved that make neurons sensitiveto light. Invasive optical modalities for stimulation may also involveimplanting optical fibers into the brain regions. Combined non-invasiveand invasive realizations, such as implantation of magnetic particlesthat then respond to applied magnetic fields, would also be reasonable.

Turning to FIG. 16, the example PTSD patient's brain 1002 is shown withimplant sites in various brain regions to modulate brain activity in theemotion regulation and fear extinction functional domains. The examplePTSD patient with impaired fear extinction may have deficits localizedin a first brain region 1004, such as the vmPFC, a second brain region1006, such as the dACC, and a third brain region 1008, such as theamygdala. The vmPFC brain region 1004 and the amygdale 1008 are deepstructures of the brain 1002, and the dACC brain region 1006 issuperficial. Thus, brain activity may be modulated by placing electrodessurgically into any of the brain regions 1004, 1006, 1008, using agrid-type electrode 1010 for the superficial structure (i.e., the dACCbrain region 1006) and deep brain stimulation probes 1012, 1014 for thevmPFC brain region 1004 and the amygdala brain region 1008,respectively.

Returning to FIG. 6, once stimulation is applied to the identified brainregions, the stimulation may be adjusted in real-time to suppress theidentified abnormal signals within the target brain regions at processblock 214. The stimulation may be directly adjusted using, for example,real-time recordings of brain electrical signals for closed loopcontrol.

In one non-limiting example, if the patient receives an invasivestimulation modality including implanted electrodes, such as theelectrodes 16 of system 10 shown in FIG. 2, and a neuro-stimulationpulse generator, such as the current pulse generator 54 shown in FIG. 2,to deliver electricity, stimulation may be delivered in a closed loopfashion. That is, the system may directly monitor the brain's electricalactivity at the implant sites, and may alter the stimulation dose ateach site based on observations throughout the network. In someembodiments, monitoring electrical activity may be done optically, forexample, through genetically encoded voltage reporters. Monitoring mayattempt to directly infer the patient's emotional state, to respond tothe patient's intentional commands, or to merge both types of monitoringinto a hybrid system.

In order to adequately adjust stimulation in the various brain regionsat process block 214, an emotional decoding algorithm may be stored onthe controlling hub 12 of system 10 (see FIG. 1) that can infer thepatient's current emotional/symptom state from brain activity. Theemotional decoding algorithm may utilize the time-resolved imaging andrecording data acquired at process block 210. For example, it may beknown that theta-frequency (4-8 Hz) coupling between the vmPFC brainregion and the amygdala brain region plays a role in the encoding andextinction of fear memories. For the example PTSD patient, the emotionaldecoding algorithm may “decode” the patient's current capacity for fearextinction, either from relative theta power in the vmPFC and dACC brainregions, or more likely from theta-band coherence between the dACC,vmPFC, and amygdala brain regions.

In an alternative embodiment, the patient's overall level of fear may befocused on, and the amygdala activity (likely in the high-gamma 65-200Hz band) may be monitored directly as a proxy for level of emotionaldistress. This could then be regulated with the help of an intentiondecoder, as will be described in further detail below. Regardless of themethod used to adjust stimulation, there is a signal that the controllerhub 12 (see FIG. 1) is programmed to sense. The controller hub 12 maythen adjust stimulation (e.g., the stimulation intensity or differentparameters) until the signal returns to a pre-defined range. For theexample PTSD patient with impaired fear regulation, amygdala activitymay be directly sensed, and the controller hub 12 may be configured tokeep the amygdala activity within the pre-defined range (e.g., apre-measured, non-anxious baseline). When amygdala activity exceeded thepre-defined range, the controller hub 12 may be configured to interpretthis as the patient experiencing ungovernable fear, and would drive theimplanted neurostimulator to shut down amygdala.

This closed-loop control may not be limited to brain electrical signals.For example, fear may be related to a number of autonomic signals thatare detectable non-invasively, including heart rate variability, skinconductance, and pupil diameter. These autonomic signals may be measuredby a non-invasive device, such as the sensors 58 shown in FIG. 2, andtransmitted to the controller hub 12 via a communication protocol. Thecontroller hub 12 may then integrate these peripheral biomarkers withits recorded neural signals to determine adequate stimulationadjustments to suppress the abnormal signals in the brain regions.

Once the stimulation is adjusted at process block 214, intentionalcontrol over the neurostimulation system (e.g., the BCI system 10) andhow the closed-loop algorithm is applied may be provided to the patientat process block 216. By providing patient intentional control,difficulties with over-control and emotional numbing, for example, maybe circumvented. The closed-loop controller may be anemotional/affective brain-computer interface (BCI), such as the BCIfound in the system 10 of FIGS. 1 and 2, that actively monitors thepatient's emotional experience as expressed in the patient's brainactivity.

Advantageously, the system 10 may have the ability to deploy the abovedescribed algorithms and have the algorithms available as the symptomsare experienced in the daily living of the patient. The patient may begiven a degree of control over the stimulator's operation to gate itsability to turn stimulation on/off or to modulate the intensity. Forexample, FIG. 17 shows an example of a closed-loop affective decoder2012 and brain stimulator 2054 for psychiatric indications. Theaffective decoder 2012 may be monitoring the vmPFC brain region 2004 anddecoding the patient's intention to activate the stimulator 2054. Thestimulator 2054 shown in FIG. 17 is operating in the limbic circuit2014. The stimulator 2054 may be tracking and responding to activity inthe amygdala brain region 2008. Command signals recorded in the vmPFCbrain region 2004 may modulate the stimulator's 2054 behavior. WhenvmPFC activity is high, the stimulator 2054 may remain active and may beaggressive in emotional regulation. When vmPFC activity is low, thestimulator 2054 may allow amygdala activity to vary freely withoutintervening. Importantly, vmPFC activity is under the patient's directand intentional control, meaning that the patient chooses what thestimulator 2054 will do.

More specifically, the system may use the patient's brain signals as aread-out of what he/she wants the stimulator to be doing, then use thatto guide application of the closed-loop system. This may be called ahybrid BCI because there is an autonomous part (e.g., the emotionaldecoder) and a patient-controllable part (the intention decoder), andthe two are coupled together to achieve adequate clinical performance.In some realizations, the emotional decoder may not be needed, or may berelatively trivial (e.g., the monitoring of a single channel in a singlebrain area).

EXAMPLE

The prefrontal cortex may be a natural source of intentional emotionregulation signals, and therefore the BCI controller may not only inferthe patient's emotional state, but may also decode volitionallycontrolled brain signals. The desire to suppress or amplify emotionalexperiences is already contained in PFC activity, and that activity maycorrelate directly with patients' ability to succeed in emotionregulation. Further, PFC neurons are flexible and may regularly re-tunethemselves into new ensembles, encoding complex features of multipletasks. Thus, given that plasticity may be important for successfulaffective decoding and control, a BCI that decodes signals from highlyplastic cortex is more likely to succeed, because the brain can morereadily re-tune to communicate a clinical need.

Unifying the themes above, one approach to affective BCI for closed-loopDBS is to record volitionally controlled signals from PFC, then use thatactivity as a reflection of the patient's desire to adjust stimulationparameters. This would not directly decode emotion, but instead could beseen as decoding an intention towards emotional regulation. That signalis well known to exist in PFC based on neuroimaging data. An affectivedecoder, similar to those already demonstrated, may then classify thepatient's current emotional state and serve as a feedback signal for anadaptive stimulation algorithm. Alternatively, the volitional PFCactivity could itself be that feedback signal. This ‘direct control’ BCIapproach is known to be capable of decoding one or more degrees offreedom, which should be sufficient to control the parameters of mostclinical brain stimulators. A rodent proof-of-concept studydemonstrating this PFC-based affective BCI strategy is presented below,as well as how the strategy may be scaled and adapted to achieve thegoal of closed-loop emotional brain stimulation. Importantly, althoughthe examples below describe control based on the firing of singleneurons, a wide range of signals may be used to encode and infer thepatient's intention. This would include power in a variety of frequencybands, the connectivity between multiple brain areas, the summed firingof multiple neurons within one or more brain areas, and the like.

Turning to FIGS. 18A and 18B, a block diagram of an experimental setupand a schematic of an affective BCI algorithm are shown. Theexperimental setup of FIG. 18A includes a tethered recording, decoding,and stimulating system. Modular components control each function,permitting modification of experimental setup for alternate decoders orstimulation schemes. As illustrated, neural data flow from the animal inthe operant chamber, through dedicated on-line spike sorting andbehavioral tagging, to a desktop PC for processing and storage. Based onneural signals, the PC controls the behavioral system and stimulator,which deliver neurofeedback and brain stimulation to the animal.

Briefly, adult female Long-Evans rats were implanted with arrays ofrecording electrodes in the PFC (prelimbic and infralimbic cortices),and stimulating deep-brain electrodes. Stimulating electrodes targetedthe medial forebrain bundle (MFB), a structure within the reward pathwaywhere electrical stimulation is known to be reinforcing. MFB is a targetfor human clinical trials in DBS for depression, highlighting itsrelevance as a stimulation site in this closed-loop testbed. For thiswork, however, the MFB is used for its reinforcing properties, and notas a candidate treatment site for human translation.

Affective dysregulation and a desire to activate or alter brainstimulation can be decoded from volitionally controlled PFC activity. Todemonstrate that rodents can learn to use an intention-decoding BCI todrive brain stimulation, the animals may be trained to use an auditoryBCI. As shown in FIG. 18B, activity of a single unit recorded from thePFC may be converted to an auditory cursor using the BCI algorithm. Rawdata may be recorded from the PFC and sorted online, after which spikerates may be estimated and converted to an audio cursor for the animal.The firing rate of the decoded unit may control the frequency of a tonepresented to the animal, implementing a paradigm similar to aneurofeedback system. To model the plasticity component, the BCI was nottrained or otherwise pre-tuned to neural firing modulation. Rather, atthe start of each session a unit was selected and mapped into the BCIwith fixed parameters. Animals were thus required to learn and adapt tothe new BCI mapping each day to communicate their desire forneurostimulation.

Referring to FIG. 18C, schematics of BCI trials are shown. The animalmay be presented with auditory cues, represented as a target cue 3002and corresponding cue periods 3004, initiated in a self-paced fashion byholding PFC activity at an initial baseline 3006. Moving the audiocursor to within a window of the target cue 3002 constituted control ofthe BCI, and activated neurostimulation in the medial forebrain bundle(MFB). Failure to reach the target cue 3002 within a pre-set time mayhave led to a brief time-out. In a human clinical system, subjects maydeliberately avoid the target, and thus leave the neurostimulator off,except when the patient experiences symptoms that they wish to control.Because stimulation was reinforcing in this paradigm, the subjectlearned to use BCI to express desire for stimulation by acquiringtargets when available and communicate an affect-regulation desire.

Baseline firing 3006 of the selected PFC unit was measured at the startof each day, and dwelling the firing rate at the baseline 3006 initiateda new trial. For each trial, a tone was briefly played, and the animalhad 5-10 seconds to modulate the PFC firing rate to match her audiofeedback cursor to the target cue 3002. Targets were based on thestandard deviation (SD) of the baseline firing rate 3006, and requiredthe animal to elevate firing rate by about 1.5 SD. Successful targetacquisition, as shown in the top graph of FIG. 18C, triggered a phasicburst 3008 of MFB stimulation, whereas failure target acquisition, asshown in the bottom graph of FIG. 18C, did not. That is, each time theanimal successfully controlled PFC neural firing, electrical stimulationwas delivered within her limbic circuit. Trials were followed by brieftime-outs, after which a new trial became available for self-initiation.This demonstrates the concept that volitional PFC activity can bedecoded via a BCI and used as a signal of desire to activate aneurostimulator.

In the above described system, the animals successfully learned tocontrol the PFC BCI to trigger MFB stimulation. For example, FIG. 19shows successful PFC BCI control for limbic stimulation. A first line4002 represents the actual target acquisition rate over a single testingsession. A second dashed line 4004 and a horizontal solid line 4006represent on-line (batch′) and off-line (bootstrap′) estimates ofchance-level performance, respectively. BCI target acquisition, and thussuccessful delivery of reinforcing neurostimulation, rises to a peak4008 above both measures of chance. Performance is sustained for over 20minutes before the performance declines, possibly due to fatigue.

Target-acquisition rates were initially low as the animal learned thenew decoder, then rapidly increased and were sustained for over 20minutes, as just described. During this core performance period, whenthe animal had learned the decoder and was actively attending to theBCI, target hit rates remained well above both on-line (catch trials)4004 and off-line (bootstrap replication) measures 4006 of chance. Theanimals generally learned to control newly isolated PFC units afterabout 20-40 minutes of practice. About eighty percent of tested sites inthe PFC were controllable, consistent with the hypothesis that arbitraryneurons can be used for affective decoding by exploitingneuroplasticity.

Referring to FIG. 20, control of PFC neurons in the BCI paradigm washighly specific. FIG. 20 shows two examples of peri-stimulus dischargerates on single channels involved in the PFC BCI. Examples are takenfrom the same animal, on two successive days, during which differentunits were controlled. Layout of subfigures within each example reflectsrelative location of individual electrodes within the cortex. In eachpanel, the channel controlling the BCI (A, channel 8; B, channel 14)shows a sharp rise into the middle of the target (arrowhead) followed byan equally sharp return to baseline once success is achieved. Otherchannels show little to no peri-event modulation, consistent withspecific control of PFC unit selected for the BCI.

FIG. 20 further shows the averaged firing rates of multiplesimultaneously recorded PFC units during two consecutive training days,time-locked to successful acquisition of a BCI target and delivery ofreinforcing brain stimulation. Substantial modulation in discharge rateoccurred on the channel used for the BCI (arrowhead). That channel,which was changed between these two sessions, shows a sharp rise intothe target and equally sharp return to baseline after the onset of brainstimulation. The other, non-decoded channels show no average time-lockedmodulation. This provides further evidence that animals were able tospecifically remap arbitrary PFC neurons to match the BCI decoder,establishing an ‘emotional communication channel’ to indicate theirintent to receive neurostimulation. Intentional control of PFC units isalso demonstrated by the channel-specific modulation of FIG. 20. This isevidence that animals were executing a learned and specific skill toachieve control of the BCI.

Psychiatric patients frequently report that they recognize emotionalsymptoms as ‘not my real self’ (ego-dystonic) and attempt to suppressthem, clear evidence that they would be able to recognize the need toactivate a stimulator. Some are even able to learn new cognitive skillsthat enable active suppression of symptoms. A responsive BCI basedstimulator, such at the one just described, may effectively amplifythose skills and achieve what some patients are unable to do on theirown.

Work using similar operant paradigms has shown that artificial couplingof activity between two brain sites or between brain and spinal cord caninduce long-term increases in functional connectivity. A hybrid BCI suchas that described above could be targeted not to directly controlsymptoms, but to train and strengthen the user's internal regulatorycircuits. This device would then be used for a limited time period torepair an identified brain deficit.

Although the invention has been described in considerable detail withreference to certain embodiments, one skilled in the art will appreciatethat the present invention can be practiced by other than the describedembodiments, which have been presented for purposes of illustration andnot of limitation. Therefore, the scope of the appended claims shouldnot be limited to the description of the embodiments contained herein.

What is claimed is:
 1. A method for diagnosing mental or emotionaldisorders comprising: a) administering, using a computer interface, atransdiagnostic assessment to a patient, the transdiagnostic assessmentincluding at least one psycho-physical task; b) receiving a series ofinput data related to a brain of the patient acquired while the patientperforms the at least one psycho-physical task; c) determining animpairment corresponding to the patient along a set of functionaldomains; d) recording, using a signal recorder, at least one ofelectrical, magnetic, optical, bio-acoustic, brain derived or bodyderived activity from the patient; e) identifying, from the at least onepsycho-physical task, a deviation in the set of functional domains ascompared to a predetermined control; f) identifying brain regions andsignal characteristics corresponding to the identified deviation, usingthe input data; and g) applying, using at least one stimulation device,stimulation to the identified brain regions to alter the at least one ofelectrical, magnetic, optical, bio-acoustic, brain derived or bodyderived activity in the brain regions.
 2. The method of claim 1, whereinthe at least one psycho-physical task is designed to probe functioningin at least one brain region of the patient.
 3. The method of claim 1,wherein the series of input data includes at least one of electricalactivity, magnetic activity, acoustic activity or medical images relatedto the brain of the patient.
 4. The method of claim 1, whereinidentifying brain regions and signal characteristics corresponding tothe identified deviation further includes using general trends in theactivity known to a skilled user.
 5. The method of claim 1, wherein theat least one stimulation device includes at least one of an invasivestimulation device or a non-invasive stimulation device.
 6. The methodof claim 1, further comprising the step of obtaining additional imagingdata of at least one of the brain regions or additional body recordingsto identify abnormal features of the brain derived activity.
 7. Themethod of claim 6, wherein the abnormal features of the brain derivedactivity include at least one of electrical signals from event-relatedpotentials related to features of the at least one psycho-physical task,event-related synchronizations and de-synchronizations in a frequencydomain related to features of the at least one psycho-physical task,changes in phase of oscillatory brain activity, coupling andconnectivity between different brain regions, or interactions offunctional brain rhythms within and between the different brain regions.8. The method of claim 1, further comprising the step of adjusting,using an emotional decoding algorithm stored on a controller,stimulation to suppress abnormal signals in the brain regions, whereinthe stimulation is adjusted in real-time to suppress the identifiedabnormal signals within the brain regions.
 9. The method of claim 1,wherein the functional domains include at least one of fear, rewardmotivation, emotion regulation, decision making/impulsivity,attention/preservation, or cognitive capacity.
 10. The method of claim1, wherein the at least one psycho-physical task includes at least oneof a fear extinction task, an aversion reward conflict task, an emotionconflict resolution task, a gambling task, a cognitive interferencetask, or an associative learning task.
 11. The method of claim 1,wherein the signal recorder includes at least one of structural magneticresonance imaging (MRI), functional magnetic resonance imaging (fMRI),positron emission tomography (PET), single-photon emission computedtomography (SPECT), electroencephalography (EEG), magnetoencephalography(MEG), near-infrared spectroscopy (NIRS), ultrasound, fluorescentemission, or surgically implanted electrodes to measure the at least oneof electrical, magnetic, bio-acoustic, brain derived or body derivedactivity.
 12. The method of claim 1, wherein recording the at least oneof electrical, magnetic, bio-acoustic, brain derived or body derivedactivity from the patient is accomplished by recording at least onebiomarker.
 13. The method of claim 12, wherein the at least onebiomarker includes at least one of heart rate, eye movements, blinks,eye pupil diameter, skin conductance, galvanic skin response,respiratory rate, recorded speech, or electromyography.
 14. The methodof claim 1, further comprising the step of acquiring, from a database ofpatients without evident psychiatric impairment, transdiagnosticassessments to determine the predetermined control.
 15. A system fordiagnosing mental or emotional disorders comprising: a computerinterface for administering a transdiagnostic assessment to a patient,the transdiagnostic assessment including at least one psycho-physicaltask; an acquisition device for acquiring a series of input data relatedto a brain of the patient while the patient performs the at least onepsycho-physical task to determine an impairment corresponding to thepatient along a set of functional domains; a signal recorder forrecording at least one of electrical, magnetic, bio-acoustic, brainderived or body derived activity from the patient; a processor coupledto the computer interface and configured to identify, from the at leastone psycho-physical task, a deviation from the set of functional domainsas compared to a predetermined control, and identify brain regions andsignal characteristics from the input data corresponding to theidentified deviation; and at least one stimulation device for applyingstimulation to the identified brain regions to alter the at least one ofelectrical, magnetic, bio-acoustic, brain derived or body derivedactivity in the brain regions.
 16. The system of claim 15, wherein theseries of input data includes at least one of electrical activity,magnetic activity, acoustic activity or medical images related to thebrain of the patient.
 17. The system of claim 15, wherein the processoris further configured to identify brain regions and signalcharacteristics corresponding to the identified deviation using generaltrends in the activity known to a user.
 18. The system of claim 15,wherein the at least one stimulation device includes at least one of aninvasive stimulation device or a non-invasive stimulation device. 19.The system of claim 15, wherein the at least one psycho-physical task isdesigned to probe functioning in at least one brain region of thepatient.
 20. The system of claim 15, further including an emotionaldecoding algorithm stored on a controller for adjusting stimulation tosuppress abnormal signals in the brain regions, wherein the stimulationis adjusted in real-time to suppress the identified abnormal signalswithin the brain regions.
 21. The system of claim 15, wherein thefunctional domains include at least one of fear, reward motivation,emotion regulation, decision making/impulsivity,attention/perseveration, or cognitive capacity.
 22. The system of claim15, wherein the at least one psycho-physical task includes at least oneof a fear extinction task, an aversion reward conflict task, an emotionconflict resolution task, a gambling task, a cognitive interferencetask, or an associative learning task.
 23. The system of claim 15,wherein the signal recorder includes at least one of structural magneticresonance imaging (MRI), functional magnetic resonance imaging (fMRI),positron emission tomography (PET), single-photon emission computedtomography (SPECT), electroencephalography (EEG), magnetoencephalography(MEG), near-infrared spectroscopy (NIRS), ultrasound, fluorescentemissions, or surgically implanted electrodes to measure the electricalactivity.
 24. The system of claim 15, wherein the signal recorder isconfigured to record electrical activity from the patient by recordingat least one biomarker.
 25. The system of claim 24, wherein the at leastone biomarker includes at least one of heart rate, eye movements,blinks, eye pupil diameter, skin conductance, galvanic skin response,respiratory rate, recorded speech, or electromyography.
 26. The systemof claim 15, further comprising a database of patients without evidentpsychiatric impairment accessible by the processor to acquiretransdiagnostic assessments to determine the predetermined control.